Package weka.classifiers.trees.m5
Class M5Base
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.trees.m5.M5Base
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,AdditionalMeasureProducer
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public abstract class M5Base extends Classifier implements AdditionalMeasureProducer, TechnicalInformationHandler
M5Base. Implements base routines for generating M5 Model trees and rules.The original algorithm M5 was invented by Quinlan:
Yong Wang made improvements and created M5':
Quinlan J. R. (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence. 343--348. World Scientific, Singapore.
Wang, Y and Witten, I. H. (1997). Induction of model trees for predicting continuous classes. Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague. Valid options are:-U
Use unsmoothed predictions.-R
Build regression tree/rule rather than model tree/rule- Version:
- $Revision: 6260 $
- Author:
- Mark Hall (mhall@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description M5Base()
Constructor
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
Generates the classifier.java.lang.String
buildRegressionTreeTipText()
Returns the tip text for this propertydouble
classifyInstance(Instance inst)
Calculates a prediction for an instance using a set of rules or an M5 model treejava.util.Enumeration
enumerateMeasures()
Returns an enumeration of the additional measure namesjava.lang.String
generateRulesTipText()
Returns the tip text for this propertyboolean
getBuildRegressionTree()
Get the value of regressionTree.Capabilities
getCapabilities()
Returns default capabilities of the classifier, i.e., of LinearRegression.RuleNode
getM5RootNode()
double
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measuredouble
getMinNumInstances()
Get the minimum number of instances to allow at a leaf nodejava.lang.String[]
getOptions()
Gets the current settings of the classifier.TechnicalInformation
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.boolean
getUnpruned()
Get whether unpruned tree/rules are being generatedboolean
getUseUnsmoothed()
Get whether or not smoothing is being usedjava.lang.String
globalInfo()
returns information about the classifierjava.util.Enumeration
listOptions()
Returns an enumeration describing the available optionsdouble
measureNumRules()
return the number of rulesjava.lang.String
minNumInstancesTipText()
Returns the tip text for this propertyvoid
setBuildRegressionTree(boolean newregressionTree)
Set the value of regressionTree.void
setMinNumInstances(double minNum)
Set the minimum number of instances to allow at a leaf nodevoid
setOptions(java.lang.String[] options)
Parses a given list of options.void
setUnpruned(boolean unpruned)
Use unpruned tree/rulesvoid
setUseUnsmoothed(boolean s)
Use unsmoothed predictionsjava.lang.String
toString()
Returns a description of the classifierjava.lang.String
unprunedTipText()
Returns the tip text for this propertyjava.lang.String
useUnsmoothedTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
returns information about the classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-U
Use unsmoothed predictions.-R
Build a regression tree rather than a model tree.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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unprunedTipText
public java.lang.String unprunedTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setUnpruned
public void setUnpruned(boolean unpruned)
Use unpruned tree/rules- Parameters:
unpruned
- true if unpruned tree/rules are to be generated
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getUnpruned
public boolean getUnpruned()
Get whether unpruned tree/rules are being generated- Returns:
- true if unpruned tree/rules are to be generated
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generateRulesTipText
public java.lang.String generateRulesTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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useUnsmoothedTipText
public java.lang.String useUnsmoothedTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setUseUnsmoothed
public void setUseUnsmoothed(boolean s)
Use unsmoothed predictions- Parameters:
s
- true if unsmoothed predictions are to be used
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getUseUnsmoothed
public boolean getUseUnsmoothed()
Get whether or not smoothing is being used- Returns:
- true if unsmoothed predictions are to be used
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buildRegressionTreeTipText
public java.lang.String buildRegressionTreeTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getBuildRegressionTree
public boolean getBuildRegressionTree()
Get the value of regressionTree.- Returns:
- Value of regressionTree.
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setBuildRegressionTree
public void setBuildRegressionTree(boolean newregressionTree)
Set the value of regressionTree.- Parameters:
newregressionTree
- Value to assign to regressionTree.
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minNumInstancesTipText
public java.lang.String minNumInstancesTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setMinNumInstances
public void setMinNumInstances(double minNum)
Set the minimum number of instances to allow at a leaf node- Parameters:
minNum
- the minimum number of instances
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getMinNumInstances
public double getMinNumInstances()
Get the minimum number of instances to allow at a leaf node- Returns:
- a
double
value
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier, i.e., of LinearRegression.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Generates the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- set of instances serving as training data- Throws:
java.lang.Exception
- if the classifier has not been generated successfully
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classifyInstance
public double classifyInstance(Instance inst) throws java.lang.Exception
Calculates a prediction for an instance using a set of rules or an M5 model tree- Overrides:
classifyInstance
in classClassifier
- Parameters:
inst
- the instance whos class value is to be predicted- Returns:
- the prediction
- Throws:
java.lang.Exception
- if a prediction can't be made.
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toString
public java.lang.String toString()
Returns a description of the classifier- Overrides:
toString
in classjava.lang.Object
- Returns:
- a description of the classifier as a String
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enumerateMeasures
public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Returns:
- an enumeration of the measure names
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getMeasure
public double getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- Parameters:
additionalMeasureName
- the name of the measure to query for its value- Returns:
- the value of the named measure
- Throws:
java.lang.Exception
- if the named measure is not supported
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measureNumRules
public double measureNumRules()
return the number of rules- Returns:
- the number of rules (same as # linear models & # leaves in the tree)
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getM5RootNode
public RuleNode getM5RootNode()
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